BP神经网络在振动筛故障诊断中的应用  

Application of BP Neural Network to Fault Diagnosis of Vibrating Sieve

作　　者：秦太龙[1] 时建峰[1] 程珩[1] 

机构地区：[1]太原理工大学机械电子工程研究所,山西太原030024

出　　处：《煤矿机电》2008年第2期42-43,46,共3页Colliery Mechanical & Electrical Technology

基　　金：国家自然科学基金资助项目(编号:50335030);山西省自然科学基金资助项目(编号:20051047)

摘　　要：按BP神经网络的基本原理和算法,确定了振动筛的BP神经网络结构,用振动筛运行状态的特征量作为BP神经网络的输入,运用Matlab神经网络工具箱对该网络进行训练,得到了用于诊断的BP神经网络模型。实验结果表明,运用神经网络方法能较为准确诊断振动筛故障。According to the principle and algorithm of BP neural network, the structure of vibrating sieve can be determined. The BP neural network model for fault diagnosing is obtained by taking the characteristic variables of operating state of vibrating sieve as the input of neural network and using the Matlab neural network toolbox for the network training. The experimental result shows that the method can accurately diagnose the fault of vibrating sieve.

关 键 词：BP神经网络 振动筛故障诊断 MATLAB神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]